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polymarket

Read-onlyIdempotent

Fetch prediction market data with live per-outcome probabilities, volume, liquidity, best bid/ask, and end dates; search by topic or sort by volume, liquidity, or soonest ending.

Instructions

[Trading Data API] Polymarket prediction markets with live probabilities: question, per-outcome probability, 24h and total volume, liquidity, best bid/ask, one-day price change and end date. Top markets by volume, liquidity or soonest end, or free-text search.

Example: /v1/polymarket?q=bitcoin&limit=5

Use when you want Polymarket markets with live per-outcome probabilities and volume. Not for Kalshi markets (use kalshi_probability).

Behavior: read-only. Priced via x402 (USDC on Base) or free with WICKEDAPI_API_KEY; with neither, you get HTTP 402 with the decoded price in payment_required (a price quote, not a failure).

Input Schema

NameRequiredDescriptionDefault
qNoOptional. Free-text search, e.g. bitcoin, fed rate, election.
sortNoOptional. volume (default), liquidity or ending. Ignored when q is set.
limitNoOptional. 1-25 (default 10).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.7.2

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the annotations, the description discloses the payment/auth behavior: x402 USDC on Base, free with `WICKEDAPI_API_KEY`, and an HTTP 402 price quote when neither is present, which is explicitly framed as not a failure. This is exactly the kind of operational context an agent needs before invoking the tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded and efficient, with the key purpose, usage boundary, example, and payment behavior in a compact block. There is minor repetition around 'Polymarket markets' and a slightly noisy API prefix, but nothing that materially hurts readability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description compensates by enumerating the key returned fields — question, per-outcome probability, volumes, liquidity, bid/ask, price change, end date. It also covers the important payment failure mode, making the definition complete for a read-only data query tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so `q`, `sort`, and `limit` are already well documented. The description adds a useful example and maps sort concepts to volume/liquidity/ending, but it does not add much parameter syntax or semantics beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific resource and capability: Polymarket prediction markets with live per-outcome probabilities, volume, liquidity, bid/ask, price change, and end date. It also explicitly distinguishes this from Kalshi markets, so an agent can route between siblings without opening schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It says exactly when to use this tool, when not to use it, and which alternative to call for Kalshi data (`kalshi_probability`). The supported modes — top by volume, liquidity, soonest end, or free-text search — are clearly stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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